Hello,

I had previously asked a similar question, but it was partly unanswered, so I'm narrowing down the scope of this questions to two specific parts.
A bit of a background to my questions:
I'm coming from the field of strategic management, and the prevalent technique is large sample analysis is fixed-effects or random-effects regression whenever the structure of data allows.
Given that note, I see many papers in "A Journals" that treat dependent variables such as R&D intensity or R&D expenditure with either fixed-effects or random-effects regression.
My specific questions are as follows:

1. How "wrong" it is, technically, to use such models for positive dependent variables? Specifically, in case of R&D intensity (R&D expenditure divided by sales), the value rarely goes above 2, so I think xtreg should give somewhat of an incorrect estimation?

2. How "correct" it is, technically, to use xtpoisson or glm with a log link for such dependent variables? I've compared the results of xtpoisson and glm poisson family with xttobit (with ul and ll defined), and they're highly consistent, signaling to me that these (xtpoisson and glm) are far better estimators than xtreg.

My significant concern, leading me to asking the above questions, is that some relationships are significant using xtreg, but not xtpoisson, and vice versa. I see even the same authors picking methods for the same DV in different papers arbitrarily, making me very confused as a junior and inexperienced researcher.
Your answers are much appreciated in advance, and any reference on the "correctness" or "incorrectness" on such scenarios is highly appreciated.

Thanks.